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Show HN: TurboQuant-WASM – Google's vector quantization in the browser

(github.com)

I tried TQ for vector search and my findings is not good, it is not worth it if you cannot use GPU, however I got same quality of search as 32f using 8bit quant

I wrote ann ext for sqlite, using tq, I do save a lot on space but 32f is still faster despite everything I have tried

code here https://github.com/netdur/munind/tree/main/src/tq

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So i assumed it would get crushed by OPQ (which requires training)
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you’re right that 32f is faster on raw query time, quantization adds extra step. main benefit on download size since gzip won’t help much, which matters most in browser contexts
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Very cool. I added the new multi embedding 2 model to my site the other week from google

I guess need to dig into this and see if it’s faster and has more use cases! Thanks for publishing your work

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Awesome! Also love the gaussian splat demo, cool use case!
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Sloppiest slop I've seen in a couple weeks:

- fork of a fork of a quantization technique

- Only contribution is...compiling JS to WASM by default?

- suspicious burst of ~nothing comments from new accounts

- 6 comments 7 hours in, 4 flagged/dead, other 2 also spammy, confused and making category errors at best, at worst, more spam.

- Demo shows it's worse: 800 ms instead of 2.6 ms for text embedding search

- "but it saves space" - yes! 1.2 MB in RAM instead of 7.2 MB to turn search into 1s on a MacBook Pro M4 Max, instead of sub-frame duration.

- It's not even wrong to do this with the output embeddings, there's way more obvious ways to save space that don’t affect retrieval time this much

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